Objective methods for assessing perceptual image quality commonly examine the visibility of the errors. An error is the difference between an impaired image and a reference image. Both images are made up of pixels. Each pixel is associated with three color values also called color components for example RGB or YUV. Bearing in mind a variety of known properties of the human visual system, an objective quality metric is able to give a relevant quality value regarding a ground truth. Classical methods used to assess the quality of an impaired image with respect to a reference image such as the one described in the document from Le Callet et al. entitled “A robust quality metric for color image quality assessment” published in ICIP2003 comprise the following steps depicted on FIG. 1:                A transformation step 10;        A contrast sensitivity function (CSF) filtering step 11;        A perceptual subband decomposition (PSD) step 12;        A masking step 13; and        An error pooling step 14.        
The transformation step 10 consists in transforming each of the three color components of both the reference and the impaired images into a perceptual color space, for example the Krauskopf color space (A, Cr1, Cr2) which is more relevant from the human visual system point of view. The transformation step 10 simulates the color opponent process. The opponent color theory suggests that there are three opponent channels: red versus green (channel Cr1), blue versus yellow (channel Cr2), and black versus white (channel A). The latter channel is achromatic and detects light-dark variation, or luminance.
The filtering step 11 consists in filtering the three components of both the impaired and the reference images using the CSF function. CSF deals with the fact that a viewer is not able to perceive all the details in his visual field with the same accuracy. More precisely, the viewer sensitivity depends of the spatial frequencies and the orientation of visual stimuli. For examples, if the frequency of the visual stimuli is too high, a viewer is not able to recognize the stimuli pattern anymore. Moreover his sensitivity is higher for the horizontal and vertical structures.
The perceptual subband decomposition step 12 consists in decomposing each of the three filtered components of both the impaired and the reference images into N subbands in the Fourier domain. The perceptual subband decomposition step 12 simulates the different populations of visual cells (cones, . . . ). Therefore, each subband may be regarded as the neural image generated by a particular population of visual cells tuned to both a particular orientation and a particular frequency. The decomposition, based on different psychophysics experiments, is obtained by carving up the frequency domain both in spatial radial frequency and orientation. The perceptual decomposition of one luminance component leads to 17 psychovisual subbands distributed on 4 crowns as shown on FIG. 1. Four crowns of spatial frequency are labelled from I to IV on FIG. 1:                I: spatial frequencies from 0 to 1.5 cycles per degree;        II: spatial frequencies from 1.5 to 5.7 cycles per degree;        III: spatial frequencies from 5.7 to 14.2 cycles per degree;        IV: spatial frequencies from 14.2 to 28.2 cycles per degree.        
The masking step 13 deals with the modulation of the sensitivity of the eyes regarding the content of the image. It takes into account the fact that a coding artefact is more visible in a flat region (featured by a weak masking capability) than in a highly textured region (featured by a strong masking capability).
The error pooling step 14 combines the error signals coming from different modalities into a single quality score.
The perceptual subband decomposition step 12 is achieved in the Fourier domain. Such PSD is depicted on FIG. 2. This decomposition is known from the document from Senane et al entitled “The computation of visual bandwidths and their impact in image decomposition and coding” published in 1993 in the International Conference and signal Processing Applications and Technology.
Since most images have a resolution that is not a power of two, the time required for applying the Fourier transform and to perform the subband decomposition is high.